-
Notifications
You must be signed in to change notification settings - Fork 0
/
config.py
167 lines (151 loc) · 8.79 KB
/
config.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
import argparse
from paths import PROJECT_ROOT
parser = argparse.ArgumentParser(description='Train prototypical networks')
# data args
parser.add_argument('--data.train', type=str, default='cu_birds', metavar='TRAINSETS', nargs='+', help="Datasets for training extractors")
parser.add_argument('--data.val', type=str, default='cu_birds', metavar='VALSETS', nargs='+',
help="Datasets used for validation")
parser.add_argument('--data.test', type=str, default='cu_birds', metavar='TESTSETS', nargs='+',
help="Datasets used for testing")
parser.add_argument('--data.num_workers', type=int, default=32, metavar='NEPOCHS',
help="Number of workers that pre-process images in parallel")
# model args
default_model_name = 'noname'
parser.add_argument('--model.name', type=str, default=default_model_name, metavar='MODELNAME',
help="A name you give to the extractor".format(default_model_name))
parser.add_argument('--model.backbone', default='resnet18', help="Use ResNet18 for experiments (default: False)")
parser.add_argument('--model.classifier', type=str, default='linear', choices=['none', 'linear', 'cosine'], help="Do classification using cosine similatity between activations and weights")
parser.add_argument('--model.dropout', type=float, default=0, help="Adding dropout inside a basic block of widenet")
parser.add_argument('--model.pretrained', action='store_true', help="Using pretrained model for learning or not")
# adaptor args
parser.add_argument('--adaptor.opt', type=str, default='linear', help="type of adaptor, linear or nonlinear")
# train args
parser.add_argument('--train.batch_size', type=int, default=16, metavar='BS',
help='number of images in a batch')
parser.add_argument('--train.max_iter', type=int, default=500000, metavar='NEPOCHS',
help='number of epochs to train (default: 10000)')
parser.add_argument('--train.weight_decay', type=float, default=7e-4, metavar='WD',
help="weight decay coef")
parser.add_argument('--train.optimizer', type=str, default='momentum', metavar='OPTIM',
help='optimization method (default: momentum)')
parser.add_argument('--train.learning_rate', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.0001)')
parser.add_argument('--train.sigma', type=float, default=1, metavar='SIGMA',
help='weight of CKA loss on features')
parser.add_argument('--train.beta', type=float, default=1, metavar='BETA',
help='weight of KL-divergence loss on logits')
parser.add_argument('--train.lr_policy', type=str, default='cosine', metavar='LR_policy',
help='learning rate decay policy')
parser.add_argument('--train.lr_decay_step_gamma', type=int, default=1e-1, metavar='DECAY_GAMMA',
help='the value to divide learning rate by when decayin lr')
parser.add_argument('--train.lr_decay_step_freq', type=int, default=10000, metavar='DECAY_FREQ',
help='the value to divide learning rate by when decayin lr')
parser.add_argument('--train.exp_decay_final_lr', type=float, default=8e-5, metavar='FINAL_LR',
help='the value to divide learning rate by when decayin lr')
parser.add_argument('--train.exp_decay_start_iter', type=int, default=30000, metavar='START_ITER',
help='the value to divide learning rate by when decayin lr')
parser.add_argument('--train.cosine_anneal_freq', type=int, default=4000, metavar='ANNEAL_FREQ',
help='the value to divide learning rate by when decayin lr')
parser.add_argument('--train.nesterov_momentum', action='store_true', help="If to augment query images in order to avearge the embeddings")
# evaluation during training
parser.add_argument('--train.eval_freq', type=int, default=500, metavar='EVAL_FREQ',
help='How often to evaluate model during training')
parser.add_argument('--train.eval_size', type=int, default=300, metavar='EVAL_SIZE',
help='How many episodes to sample for validation')
parser.add_argument('--train.resume', type=int, default=1, metavar='RESUME_TRAIN',
help="Resume training starting from the last checkpoint (default: True)")
# creating a database of features
parser.add_argument('--dump.name', type=str, default='', metavar='DUMP_NAME',
help='Name for dumped dataset of features')
parser.add_argument('--dump.mode', type=str, default='test', metavar='DUMP_MODE',
help='What split of the original dataset to dump')
parser.add_argument('--dump.size', type=int, default=600, metavar='DUMP_SIZE',
help='Howe many episodes to dump')
# test args
parser.add_argument('--test.size', type=int, default=600, metavar='TEST_SIZE',
help='The number of test episodes sampled')
parser.add_argument('--test.mode', type=str, choices=['mdl', 'sdl'], default='mdl', metavar='TEST_MODE',
help="Test mode: multi-domain learning (mdl) or single-domain learning (sdl) settings")
parser.add_argument('--test.type', type=str, choices=['standard', '1shot', '5shot'], default='standard', metavar='LOSS_FN',
help="meta-test type, standard varying number of ways and shots as in Meta-Dataset, 1shot for five-way-one-shot and 5shot for varying-way-five-shot evaluation.")
parser.add_argument('--test.distance', type=str, choices=['cos', 'l2'], default='cos', metavar='DISTANCE_FN',
help="feature similarity function")
parser.add_argument('--test.loss-opt', type=str, choices=['ncc', 'knn', 'lr', 'svm', 'scm'], default='ncc', metavar='LOSS_FN',
help="Loss function for meta-testing, knn or prototype loss (ncc), Support Vector Machine (svm), Logistic Regression (lr) or Mahalanobis Distance (scm)")
parser.add_argument('--test.feature-norm', type=str, choices=['l2', 'none'], default='none', metavar='LOSS_FN',
help="normalization options")
# task-specific adapters
parser.add_argument('--test.tsa-ad-type', type=str, choices=['residual', 'serial', 'none'], default='residual', metavar='TSA_AD_TYPE',
help="adapter type")
parser.add_argument('--test.tsa-ad-form', type=str, choices=['matrix', 'vector', 'none'], default='matrix', metavar='TSA_AD_FORM',
help="adapter form")
parser.add_argument('--test.tsa-opt', type=str, choices=['alpha', 'beta', 'alpha+beta'], default='alpha+beta', metavar='TSA_OPT',
help="task adaptation option")
parser.add_argument('--test.tsa-init', type=str, choices=['random', 'eye'], default='eye', metavar='TSA_INIT',
help="initialization for adapter")
# path args
parser.add_argument('--model.dir', default='', type=str, metavar='PATH',
help='path of single domain learning models')
parser.add_argument('--out.dir', default='', type=str, metavar='PATH',
help='directory to output the result and checkpoints')
parser.add_argument('--source', default='', type=str, metavar='PATH',
help='path of pretrained model')
# log args
args = vars(parser.parse_args())
if not args['model.dir']:
args['model.dir'] = PROJECT_ROOT
if not args['out.dir']:
args['out.dir'] = args['model.dir']
BATCHSIZES = {
"ilsvrc_2012": 5120,
"omniglot": 64,
"aircraft": 64,
"cu_birds": 64,
"dtd": 64,
"quickdraw": 5120,
"fungi": 64,
"vgg_flower": 64
}
LOSSWEIGHTS = {
"ilsvrc_2012": 1,
"omniglot": 1,
"aircraft": 1,
"cu_birds": 1,
"dtd": 1,
"quickdraw": 1,
"fungi": 1,
"vgg_flower": 1
}
# lambda^f in our paper
KDFLOSSWEIGHTS = {
"ilsvrc_2012": 4,
"omniglot": 1,
"aircraft": 1,
"cu_birds": 1,
"dtd": 1,
"quickdraw": 1,
"fungi": 1,
"vgg_flower": 1
}
# lambda^p in our paper
KDPLOSSWEIGHTS = {
"ilsvrc_2012": 4,
"omniglot": 1,
"aircraft": 1,
"cu_birds": 1,
"dtd": 1,
"quickdraw": 1,
"fungi": 1,
"vgg_flower": 1
}
# k in our paper
KDANNEALING = {
"ilsvrc_2012": 5,
"omniglot": 2,
"aircraft": 1,
"cu_birds": 1,
"dtd": 1,
"quickdraw": 2,
"fungi": 2,
"vgg_flower": 1
}